民航自助行李托运系统需自动检测行李加装托盘的情况。针对已装载行李的遮挡托盘目标检测问题，提出一种基于多层骨架模型的行李托盘快速检测算法。为准确描述托盘特征，通过空托盘的三维点云模型，构建托盘的边框骨架模型与平面点线模型。在线检测时，首先，采用设计的带状特征描述和提取方法，抓取托盘边框点云，并采用提出的点线引力势能自适应迭代算法，实现平面点线模型的快速粗配准，完成托盘判别。然后，在粗配准的位姿约束下，采用基于随机采样一致性的点云迭代最近点配准，实现边框骨架模型和点云模型的精确配准，得到托盘的精确位姿。大量实际行李托盘检测的对比试验验证了算法的有效性，在托盘遮挡70%以内时，仍可保持94%的正确率，检测速度超过典型算法的6倍以上。

An integral task in self-service baggage check is the detection of whether pallets are added to the baggage. Pallets loaded with the baggage are mostly obscured; therefore, a fast detection method based on a multi-layer skeleton model registration is proposed to address this issue. A point cloud skeleton model and a point-line model are constructed using a 3D point cloud model to describe the characteristics of the pallet. During online detection, the designed banded feature description is used to capture the border point clouds. Moreover, the proposed point-line potential energy iterative algorithm is utilized to register the point-line model and border points as well as to realize pallet discrimination. An iterative nearest point registration based on random sampling consistency is used to achieve accurate registration and pose calculation as well as to obtain the accurate pose of the pallet. Experimental results show that the algorithm can maintain an accuracy of 94% even when 70% of the pallet point cloud data are missing. In addition, the speed of the proposed algorithm exceeds that of a typical algorithm by more than six times.